One of the open
problems in the area of domain-specific languages is how to make
domain-specific language development easier for domain experts not versed
in a programming language design. Possible approaches are to build a
domain-specific language from parameterized building blocks or by language
(grammar) induction. Many well established techniques exist for inferring
regular languages. However, inferring context-free grammars (CFGs), which are
more expressive and powerful than regular languages, is still an open research
problem. Our approach to inferring CFG's previously made use of the genetic
programming (GP) paradigm. Preliminary work using grammar-specific heuristic
operators in tandem with non-random construction of the initial grammar
population resulted in successful induction of small grammars. Our current
focus is on incremental grammar learning.

Our project aim is
to research on methodologies of CFG induction under various constraints (use of
positive or negative samples, or both) limited not only to the GP model of
computation, but also open to investigating other models of grammar inference
like exploring the use of data mining techniques in grammar inference, the
brute-force approach (augmented with heuristics) and investigate better
algorithms and heuristics to incrementally construct grammars, all with the
hope of being able to infer real world grammars.